Difference between revisions of "2017 Winter Project Week/DWI Similarity Metrics"

From NAMIC Wiki
Jump to: navigation, search
 
(2 intermediate revisions by the same user not shown)
Line 28: Line 28:
 
|
 
|
 
<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
 
<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
−
*
+
* Discussed with Lauren and clarified some points on Diffusion Imaging. It also might help me to optimize some of my code.
 +
* Discussed with Mohsen about machine learning and how I might use it for data classification in my project.
 +
* Discussed with Henrik about information-rich patches of information in DWI data, and I learned about his LORDWI approach that could be useful to help me identify relevant information in DWI datasets.
 +
* Attended almost all machine learning events, and got a better understanding of their utility and when it might be useful to use them and when it is not necessary.
 +
* Participated in the tutorial contest.
 
|}
 
|}
  
 
==Background and References==
 
==Background and References==
 
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
 
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->

Latest revision as of 04:36, 13 January 2017

Home < 2017 Winter Project Week < DWI Similarity Metrics

Key Investigators

  • Laurent Chauvin, ETS Montreal
  • Fan Zhang, BWH
  • Lauren O'Donnell, BWH
  • Matthew Toews, ETS Montreal

Project Description

Objective Approach and Plan Progress and Next Steps
  • Investigate possible metrics for identification of information-rich patches in DWI data
  • Discuss about DWI data structure and acquisition protocol to have a better understanding of the information available in DWI datasets (e.g. single shell / multi shell, gradient orientations, b-values)
  • Identify possible metrics available to measure similarity (e.g. Mutual Information, Correlation) between datasets
  • Investigate the potential of CNN for this type of application
  • Discussed with Lauren and clarified some points on Diffusion Imaging. It also might help me to optimize some of my code.
  • Discussed with Mohsen about machine learning and how I might use it for data classification in my project.
  • Discussed with Henrik about information-rich patches of information in DWI data, and I learned about his LORDWI approach that could be useful to help me identify relevant information in DWI datasets.
  • Attended almost all machine learning events, and got a better understanding of their utility and when it might be useful to use them and when it is not necessary.
  • Participated in the tutorial contest.

Background and References

Retrieved from "https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/DWI_Similarity_Metrics&oldid=94960"